Heena Kaleem

Revenue Assurance| SQL, Power BI
• Collected raw data from servers, cleaned and organized it, and built database tables to enable
faster data retrieval and reporting
• Conducted regular maintenance, testing, debugging troubleshooting, and optimization of the
database tables using SQL queries.
• Collaborated with stakeholders to understand customer requirements and designed data
dashboards tailored to their specific needs.
• Translated complex data into user-friendly dashboards, providing actionable insights to support
decision-making.
◦ Utilized \[specific tools or technologies\] to visualize and represent data effectively.
• SQL functions, including IF conditions and others, to manipulate and transform data as per
specific business requirements
◦ Conducted data modeling and normalization to improve database performance and maintain data integrity.
• Conducted rigorous testing and validation of database tables to ensure data accuracy and
consistency.
• Developed and implemented dynamic dashboards by extracting, transforming, and loading
(ETL) data.
• Designed and optimized database queries to efficiently retrieve and manipulate large datasets,
resulting in improved dashboard performance.
• Conducted regular data analysis to identify key performance indicators (KPIs) and trends,
presenting findings to management for strategic decision-making.
• Used Power BI Desktop to generate Visualized reports. Used KPIs, Charts, Key Influencers and
assigned RLS to the client users based on their roles.
• Joined Tables from same/different data sources and used Calculated Columns and Measures as per
business needs.
◦ Identify and troubleshoot data/report related issues, resolve, and document resolution of issues.

Revenue Assurance| SQL, Power BI
• Collected raw data from servers, cleaned and organized it, and built database tables to enable
faster data retrieval and reporting
• Conducted regular maintenance, testing, debugging troubleshooting, and optimization of the
database tables using SQL queries.
• Collaborated with stakeholders to understand customer requirements and designed data
dashboards tailored to their specific needs.
• Translated complex data into user-friendly dashboards, providing actionable insights to support
decision-making.
◦ Utilized \[specific tools or technologies\] to visualize and represent data effectively.
• SQL functions, including IF conditions and others, to manipulate and transform data as per
specific business requirements
◦ Conducted data modeling and normalization to improve database performance and maintain data integrity.
• Conducted rigorous testing and validation of database tables to ensure data accuracy and
consistency.
• Developed and implemented dynamic dashboards by extracting, transforming, and loading
(ETL) data.
• Designed and optimized database queries to efficiently retrieve and manipulate large datasets,
resulting in improved dashboard performance.
• Conducted regular data analysis to identify key performance indicators (KPIs) and trends,
presenting findings to management for strategic decision-making.
• Used Power BI Desktop to generate Visualized reports. Used KPIs, Charts, Key Influencers and
assigned RLS to the client users based on their roles.
• Joined Tables from same/different data sources and used Calculated Columns and Measures as per
business needs.
◦ Identify and troubleshoot data/report related issues, resolve, and document resolution of issues.

Retail Apparel Analytics Dashboard | Python, Power BI, Data Cleaning
• Processed raw manufacturing and sales data.
• Resolved inconsistencies (e.g., missing costs, duplicate entries) and optimized datasets for analysis,
improving data accuracy by ~25%.
• Analyzed daily trends to identify top-performing categories.
• Calculated profit/loss per category by comparing manufacturing costs, selling prices, and return rates.
• Discovered discounting inefficiencies.
• Built a dynamic dashboard tracking KPIs: Daily production vs. sales, return rates, profit/loss trends, and
discount impacts.
• Added drill-down filters for category/date-level insights, enabling faster decision-making for inventory and
pricing teams.